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Inferring accurate posteriors for high-dimensional representations of thebrightness of gravitationally-lensed sources is a major challenge, in part dueto the difficulties of accurately quantifying the priors. Here, we report theuse of a score-based model to encode the prior for the inference of undistortedimages of background galaxies. This model is trained on a set ofhigh-resolution images of undistorted galaxies. By adding the likelihood scoreto the prior score and using a reverse-time stochastic differential equationsolver, we obtain samples from the posterior. Our method produces independentposterior samples and models the data almost down to the noise level. We showhow the balance between the likelihood and the prior meet our expectations inan experiment with out-of-distribution data.

Posterior samples of source galaxies in strong gravitational lenses with score-based priors